8_ANOVA1 - ANOVA 1 Introduction to Analysis of...

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ANOVA 1 Introduction to Analysis of Variance (ANOVA)
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What is ANOVA? ANOVA is short for ANalysis Of VAriance Used with 3 or more groups. E.g., caffeine study with 3 groups: No caffeine Mild dose Jolt group Level is value, kind or amount of IV Treatment Group is people who get specific treatment or level of IV
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Example Designs for ANOVA Between Subjects Design IV: 3 matches/wk G1 3 matches/wk +visualization G2 3 matches/wk + visualization + ritual G3 DV: points won 1 st match Points won 1 st match Points won 1 st match Repeated Measures (within subjects design) Grp1 Saline G1 Drug 1 G1 Drug 2 DV: Video Game Performance VGP
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Repeated Measures – Each Person Gets All Treatments Advantages of repeated measures: 1. Control for individual differences. 2. Reduced number of participants. Disadvantages: 1. Cannot always be done, e.g., sex as IV, lottery winnings as IV. 2. Reactions to treatments – carryover or contrast effects (racquetball example, surveys).
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Choosing a Design: 1. The number of Groups Use 2 groups when the study contrasts something as a whole (e.g., type of psychotherapy, workbook, flight instruction) Use 3 or more groups when The IV has lots of levels, e.g., race, geog location, therapies for phobias Treatments can be broken into meaningful pieces (training for racquetball, training for RM includes labs, workbooks, computers, etc.) Continuous variables are reduced to levels for economy (dating service similarity to hi, med, low).
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Choosing a design 2: Between vs. Within Subjects In between subjects designs, different people go to different groups. In within subjects designs, people go to multiple treatments. Control – nuisance variable can be controlled using within S design (academic performance, physical differences). Practicality – are people scarce? Are there carryover or reactivity effects? Develop 2 designs as exercise.
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Review What is the difference between within subjects and between subjects designs? What are some influences over choice of the number of levels of a factor to include in a study? What are some influences over the choice of whether to use a repeated measures design?
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Rationale for ANOVA (1) Logic of t-test. How do we know if a given mean difference is large? The difference in means is compared to the standard error of the difference in means. The size of the standard error depends on two things: the SD of the population and N, the sample size.
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Rationale for ANOVA (2) Now we have at least 3 means to test, e.g., H 0 : μ 1 = μ 2 = μ 3 . Could take them 2 at a time, but really want to test all 3 (or more) at once.
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